We present DSS-GAN, the first generative adversarial network to employ Mamba as a hierarchical generator backbone for noise-to-image synthesis. The central contribution is Directional Latent Routing (DLR), a novel conditioning mechanism that decomposes the latent vector into direction-specific subvectors, each jointly projected with a class embedding to produce a feature-wise affine modulation of the corresponding Mamba scan. Unlike conventional class conditioning that injects a global signal, DLR couples class identity and latent structure along distinct spatial axes of the feature map, applied consistently across all generative scales. DSS-GAN achieves improved FID, KID, and precision-recall scores compared to StyleGAN2-ADA across multiple tested datasets. Analysis of the latent space reveals that directional subvectors exhibit measurable specialization: perturbations along individual components produce structured, direction-correlated changes in the synthesized image.
DSS-GAN：基于Mamba骨干网络的方向性状态空间生成对抗网络用于类别条件图像合成 / DSS-GAN: Directional State Space GAN with Mamba backbone for Class-Conditional Image Synthesis
这篇论文提出了一种名为DSS-GAN的新图像生成模型，它首次将Mamba模型作为核心生成器，并通过一种创新的‘方向性潜在路由’技术，将类别信息和随机噪声沿着不同空间方向巧妙结合，从而生成了质量更高、控制更精细的类别相关图像。
源自 arXiv: 2603.17637
